SOTAVerified

Image Generation

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Papers

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Showing 3326–3350 of 6689 papers

TitleStatusHype
A Creative Industry Image Generation Dataset Based on Captions—0
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions—0
Few-Shot Concept Unlearning with Low Rank Adaptation—0
Text2Layer: Layered Image Generation using Latent Diffusion Model—0
Text2Relight: Creative Portrait Relighting with Text Guidance—0
Text2Story: Advancing Video Storytelling with Text Guidance—0
Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion—0
Few-shot Image Generation Using Discrete Content Representation—0
Text2Street: Controllable Text-to-image Generation for Street Views—0
Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration—0
Few-shot Image Generation via Information Transfer from the Built Geodesic Surface—0
Few-shot Image Generation via Style Adaptation and Content Preservation—0
Few-shot Image Generation with Elastic Weight Consolidation—0
Few-shot Semantic Image Synthesis with Class Affinity Transfer—0
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization—0
FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing Fourier Transform and Auto-encoder—0
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction—0
(f,Γ)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics—0
FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits—0
Fighting Deepfake by Exposing the Convolutional Traces on Images—0
Fill-Up: Balancing Long-Tailed Data with Generative Models—0
BigGAN-based Bayesian reconstruction of natural images from human brain activity—0
FInC Flow: Fast and Invertible k k Convolutions for Normalizing Flows—0
Finding AI-Generated Faces in the Wild—0
Finding the Subjective Truth: Collecting 2 Million Votes for Comprehensive Gen-AI Model Evaluation—0
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified